Overview

Dataset statistics

Number of variables15
Number of observations700
Missing cells140
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory82.2 KiB
Average record size in memory120.2 B

Variable types

Text1
Numeric7
Categorical6
DateTime1

Alerts

age has 35 (5.0%) missing valuesMissing
employment_type has 35 (5.0%) missing valuesMissing
annual_income has 35 (5.0%) missing valuesMissing
credit_score has 35 (5.0%) missing valuesMissing
customer_id has unique valuesUnique
loan_amount has unique valuesUnique
join_date has unique valuesUnique
repayment_history has 63 (9.0%) zerosZeros

Reproduction

Analysis started2026-02-20 08:03:50.204890
Analysis finished2026-02-20 08:03:54.454896
Duration4.25 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

customer_id
Text

Unique 

Distinct700
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2026-02-20T13:33:54.614799image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters5600
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique700 ?
Unique (%)100.0%

Sample

1st rowCUST1000
2nd rowCUST1001
3rd rowCUST1002
4th rowCUST1003
5th rowCUST1004
ValueCountFrequency (%)
cust10001
 
0.1%
cust10091
 
0.1%
cust10101
 
0.1%
cust10021
 
0.1%
cust10031
 
0.1%
cust10041
 
0.1%
cust10051
 
0.1%
cust10061
 
0.1%
cust10071
 
0.1%
cust10081
 
0.1%
Other values (690)690
98.6%
2026-02-20T13:33:54.873777image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1940
16.8%
C700
12.5%
U700
12.5%
S700
12.5%
T700
12.5%
0240
 
4.3%
4240
 
4.3%
6240
 
4.3%
2240
 
4.3%
3240
 
4.3%
Other values (4)660
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)5600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1940
16.8%
C700
12.5%
U700
12.5%
S700
12.5%
T700
12.5%
0240
 
4.3%
4240
 
4.3%
6240
 
4.3%
2240
 
4.3%
3240
 
4.3%
Other values (4)660
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1940
16.8%
C700
12.5%
U700
12.5%
S700
12.5%
T700
12.5%
0240
 
4.3%
4240
 
4.3%
6240
 
4.3%
2240
 
4.3%
3240
 
4.3%
Other values (4)660
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1940
16.8%
C700
12.5%
U700
12.5%
S700
12.5%
T700
12.5%
0240
 
4.3%
4240
 
4.3%
6240
 
4.3%
2240
 
4.3%
3240
 
4.3%
Other values (4)660
11.8%

age
Real number (ℝ)

Missing 

Distinct44
Distinct (%)6.6%
Missing35
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean42.912782
Minimum21
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2026-02-20T13:33:54.973877image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile22
Q132
median44
Q353
95-th percentile62
Maximum64
Range43
Interquartile range (IQR)21

Descriptive statistics

Standard deviation12.513303
Coefficient of variation (CV)0.2915985
Kurtosis-1.1357423
Mean42.912782
Median Absolute Deviation (MAD)11
Skewness-0.12315185
Sum28537
Variance156.58274
MonotonicityNot monotonic
2026-02-20T13:33:55.057843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
5525
 
3.6%
5324
 
3.4%
4823
 
3.3%
4621
 
3.0%
5920
 
2.9%
4520
 
2.9%
5220
 
2.9%
3619
 
2.7%
5619
 
2.7%
2119
 
2.7%
Other values (34)455
65.0%
(Missing)35
 
5.0%
ValueCountFrequency (%)
2119
2.7%
2217
2.4%
2315
2.1%
249
1.3%
2517
2.4%
2613
1.9%
2713
1.9%
2818
2.6%
2912
1.7%
308
1.1%
ValueCountFrequency (%)
6417
2.4%
637
 
1.0%
6214
2.0%
6114
2.0%
6013
1.9%
5920
2.9%
588
 
1.1%
5718
2.6%
5619
2.7%
5525
3.6%

gender
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
Male
340 
Female
335 
Other
 
25

Length

Max length6
Median length5
Mean length4.9928571
Min length4

Characters and Unicode

Total characters3495
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male340
48.6%
Female335
47.9%
Other25
 
3.6%

Length

2026-02-20T13:33:55.148617image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-20T13:33:55.228662image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
male340
48.6%
female335
47.9%
other25
 
3.6%

Most occurring characters

ValueCountFrequency (%)
e1035
29.6%
a675
19.3%
l675
19.3%
M340
 
9.7%
F335
 
9.6%
m335
 
9.6%
O25
 
0.7%
t25
 
0.7%
h25
 
0.7%
r25
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)3495
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1035
29.6%
a675
19.3%
l675
19.3%
M340
 
9.7%
F335
 
9.6%
m335
 
9.6%
O25
 
0.7%
t25
 
0.7%
h25
 
0.7%
r25
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3495
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1035
29.6%
a675
19.3%
l675
19.3%
M340
 
9.7%
F335
 
9.6%
m335
 
9.6%
O25
 
0.7%
t25
 
0.7%
h25
 
0.7%
r25
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3495
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1035
29.6%
a675
19.3%
l675
19.3%
M340
 
9.7%
F335
 
9.6%
m335
 
9.6%
O25
 
0.7%
t25
 
0.7%
h25
 
0.7%
r25
 
0.7%

region
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
South
184 
North
179 
West
176 
East
161 

Length

Max length5
Median length5
Mean length4.5185714
Min length4

Characters and Unicode

Total characters3163
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth
2nd rowEast
3rd rowWest
4th rowNorth
5th rowNorth

Common Values

ValueCountFrequency (%)
South184
26.3%
North179
25.6%
West176
25.1%
East161
23.0%

Length

2026-02-20T13:33:55.310279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-20T13:33:55.379536image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
south184
26.3%
north179
25.6%
west176
25.1%
east161
23.0%

Most occurring characters

ValueCountFrequency (%)
t700
22.1%
o363
11.5%
h363
11.5%
s337
10.7%
S184
 
5.8%
u184
 
5.8%
N179
 
5.7%
r179
 
5.7%
W176
 
5.6%
e176
 
5.6%
Other values (2)322
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)3163
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t700
22.1%
o363
11.5%
h363
11.5%
s337
10.7%
S184
 
5.8%
u184
 
5.8%
N179
 
5.7%
r179
 
5.7%
W176
 
5.6%
e176
 
5.6%
Other values (2)322
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3163
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t700
22.1%
o363
11.5%
h363
11.5%
s337
10.7%
S184
 
5.8%
u184
 
5.8%
N179
 
5.7%
r179
 
5.7%
W176
 
5.6%
e176
 
5.6%
Other values (2)322
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3163
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t700
22.1%
o363
11.5%
h363
11.5%
s337
10.7%
S184
 
5.8%
u184
 
5.8%
N179
 
5.7%
r179
 
5.7%
W176
 
5.6%
e176
 
5.6%
Other values (2)322
10.2%

education_level
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
Graduate
296 
Secondary
192 
Post-Graduate
139 
Primary
73 

Length

Max length13
Median length9
Mean length9.1628571
Min length7

Characters and Unicode

Total characters6414
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduate
2nd rowGraduate
3rd rowPost-Graduate
4th rowSecondary
5th rowGraduate

Common Values

ValueCountFrequency (%)
Graduate296
42.3%
Secondary192
27.4%
Post-Graduate139
19.9%
Primary73
 
10.4%

Length

2026-02-20T13:33:55.460361image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-20T13:33:55.534760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
graduate296
42.3%
secondary192
27.4%
post-graduate139
19.9%
primary73
 
10.4%

Most occurring characters

ValueCountFrequency (%)
a1135
17.7%
r773
12.1%
d627
9.8%
e627
9.8%
t574
8.9%
G435
 
6.8%
u435
 
6.8%
o331
 
5.2%
y265
 
4.1%
P212
 
3.3%
Other values (7)1000
15.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)6414
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1135
17.7%
r773
12.1%
d627
9.8%
e627
9.8%
t574
8.9%
G435
 
6.8%
u435
 
6.8%
o331
 
5.2%
y265
 
4.1%
P212
 
3.3%
Other values (7)1000
15.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6414
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1135
17.7%
r773
12.1%
d627
9.8%
e627
9.8%
t574
8.9%
G435
 
6.8%
u435
 
6.8%
o331
 
5.2%
y265
 
4.1%
P212
 
3.3%
Other values (7)1000
15.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6414
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1135
17.7%
r773
12.1%
d627
9.8%
e627
9.8%
t574
8.9%
G435
 
6.8%
u435
 
6.8%
o331
 
5.2%
y265
 
4.1%
P212
 
3.3%
Other values (7)1000
15.6%

employment_type
Categorical

Missing 

Distinct3
Distinct (%)0.5%
Missing35
Missing (%)5.0%
Memory size5.6 KiB
Salaried
403 
Self-Employed
191 
Unemployed
71 

Length

Max length13
Median length8
Mean length9.6496241
Min length8

Characters and Unicode

Total characters6417
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSalaried
2nd rowSalaried
3rd rowSalaried
4th rowUnemployed
5th rowSalaried

Common Values

ValueCountFrequency (%)
Salaried403
57.6%
Self-Employed191
27.3%
Unemployed71
 
10.1%
(Missing)35
 
5.0%

Length

2026-02-20T13:33:55.614871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-20T13:33:55.677552image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
salaried403
60.6%
self-employed191
28.7%
unemployed71
 
10.7%

Most occurring characters

ValueCountFrequency (%)
e927
14.4%
l856
13.3%
a806
12.6%
d665
10.4%
S594
9.3%
r403
6.3%
i403
6.3%
m262
 
4.1%
p262
 
4.1%
o262
 
4.1%
Other values (6)977
15.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)6417
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e927
14.4%
l856
13.3%
a806
12.6%
d665
10.4%
S594
9.3%
r403
6.3%
i403
6.3%
m262
 
4.1%
p262
 
4.1%
o262
 
4.1%
Other values (6)977
15.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6417
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e927
14.4%
l856
13.3%
a806
12.6%
d665
10.4%
S594
9.3%
r403
6.3%
i403
6.3%
m262
 
4.1%
p262
 
4.1%
o262
 
4.1%
Other values (6)977
15.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6417
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e927
14.4%
l856
13.3%
a806
12.6%
d665
10.4%
S594
9.3%
r403
6.3%
i403
6.3%
m262
 
4.1%
p262
 
4.1%
o262
 
4.1%
Other values (6)977
15.2%

annual_income
Real number (ℝ)

Missing 

Distinct665
Distinct (%)100.0%
Missing35
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean619136.36
Minimum1772.81
Maximum2743811.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2026-02-20T13:33:55.756192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1772.81
5-th percentile279508.33
Q1472538.45
median595289.57
Q3736665.8
95-th percentile951598.31
Maximum2743811.9
Range2742039
Interquartile range (IQR)264127.35

Descriptive statistics

Standard deviation265346.59
Coefficient of variation (CV)0.42857537
Kurtosis15.921147
Mean619136.36
Median Absolute Deviation (MAD)133235.12
Skewness2.611561
Sum4.1172568 × 108
Variance7.0408815 × 1010
MonotonicityNot monotonic
2026-02-20T13:33:55.841220image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
778076.631
 
0.1%
528074.071
 
0.1%
660221.471
 
0.1%
636766.91
 
0.1%
1138606.731
 
0.1%
6699601
 
0.1%
399189.081
 
0.1%
1742721.451
 
0.1%
244750.731
 
0.1%
583880.051
 
0.1%
Other values (655)655
93.6%
(Missing)35
 
5.0%
ValueCountFrequency (%)
1772.811
0.1%
33568.881
0.1%
50499.031
0.1%
72850.451
0.1%
94087.981
0.1%
102334.531
0.1%
150622.131
0.1%
152353.751
0.1%
156139.931
0.1%
158886.791
0.1%
ValueCountFrequency (%)
2743811.851
0.1%
2538524.71
0.1%
2274223.141
0.1%
2210141.581
0.1%
2131950.721
0.1%
1858840.531
0.1%
1742721.451
0.1%
1620097.081
0.1%
1385247.541
0.1%
1329740.611
0.1%

loan_amount
Real number (ℝ)

Unique 

Distinct700
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean310137.89
Minimum-139417.3
Maximum1088749.9
Zeros0
Zeros (%)0.0%
Negative18
Negative (%)2.6%
Memory size5.6 KiB
2026-02-20T13:33:55.930654image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-139417.3
5-th percentile47566.058
Q1192987.06
median310955.33
Q3414900.36
95-th percentile572795.98
Maximum1088749.9
Range1228167.2
Interquartile range (IQR)221913.29

Descriptive statistics

Standard deviation164238.02
Coefficient of variation (CV)0.52956453
Kurtosis1.1643587
Mean310137.89
Median Absolute Deviation (MAD)113039.02
Skewness0.35694103
Sum2.1709652 × 108
Variance2.6974128 × 1010
MonotonicityNot monotonic
2026-02-20T13:33:56.021804image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
308847.631
 
0.1%
449464.741
 
0.1%
422325.151
 
0.1%
292786.721
 
0.1%
272527.431
 
0.1%
246499.031
 
0.1%
477096.141
 
0.1%
205902.981
 
0.1%
306783.411
 
0.1%
307679.691
 
0.1%
Other values (690)690
98.6%
ValueCountFrequency (%)
-139417.31
0.1%
-136048.231
0.1%
-90470.561
0.1%
-79836.911
0.1%
-73990.621
0.1%
-73171.31
0.1%
-47421.581
0.1%
-32522.831
0.1%
-28488.261
0.1%
-22857.531
0.1%
ValueCountFrequency (%)
1088749.91
0.1%
1078332.721
0.1%
958428.861
0.1%
814382.941
0.1%
786463.951
0.1%
739963.61
0.1%
730260.461
0.1%
676733.481
0.1%
672778.491
0.1%
648006.111
0.1%

loan_purpose
Categorical

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
Car
174 
Business
139 
Other
138 
Home
137 
Education
112 

Length

Max length9
Median length5
Mean length5.5428571
Min length3

Characters and Unicode

Total characters3880
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHome
2nd rowHome
3rd rowOther
4th rowOther
5th rowBusiness

Common Values

ValueCountFrequency (%)
Car174
24.9%
Business139
19.9%
Other138
19.7%
Home137
19.6%
Education112
16.0%

Length

2026-02-20T13:33:56.110770image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-20T13:33:56.183655image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
car174
24.9%
business139
19.9%
other138
19.7%
home137
19.6%
education112
16.0%

Most occurring characters

ValueCountFrequency (%)
s417
10.7%
e414
10.7%
r312
 
8.0%
a286
 
7.4%
u251
 
6.5%
i251
 
6.5%
n251
 
6.5%
t250
 
6.4%
o249
 
6.4%
C174
 
4.5%
Other values (8)1025
26.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)3880
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s417
10.7%
e414
10.7%
r312
 
8.0%
a286
 
7.4%
u251
 
6.5%
i251
 
6.5%
n251
 
6.5%
t250
 
6.4%
o249
 
6.4%
C174
 
4.5%
Other values (8)1025
26.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3880
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s417
10.7%
e414
10.7%
r312
 
8.0%
a286
 
7.4%
u251
 
6.5%
i251
 
6.5%
n251
 
6.5%
t250
 
6.4%
o249
 
6.4%
C174
 
4.5%
Other values (8)1025
26.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3880
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s417
10.7%
e414
10.7%
r312
 
8.0%
a286
 
7.4%
u251
 
6.5%
i251
 
6.5%
n251
 
6.5%
t250
 
6.4%
o249
 
6.4%
C174
 
4.5%
Other values (8)1025
26.4%

credit_score
Real number (ℝ)

Missing 

Distinct198
Distinct (%)29.8%
Missing35
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean676.93684
Minimum521
Maximum818
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2026-02-20T13:33:56.275815image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum521
5-th percentile591.4
Q1646
median679
Q3713
95-th percentile751.8
Maximum818
Range297
Interquartile range (IQR)67

Descriptive statistics

Standard deviation49.646299
Coefficient of variation (CV)0.073339633
Kurtosis0.081721524
Mean676.93684
Median Absolute Deviation (MAD)34
Skewness-0.24805826
Sum450163
Variance2464.755
MonotonicityNot monotonic
2026-02-20T13:33:56.383832image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70512
 
1.7%
65610
 
1.4%
67710
 
1.4%
6909
 
1.3%
6959
 
1.3%
6519
 
1.3%
7068
 
1.1%
6578
 
1.1%
7018
 
1.1%
7138
 
1.1%
Other values (188)574
82.0%
(Missing)35
 
5.0%
ValueCountFrequency (%)
5212
0.3%
5301
0.1%
5371
0.1%
5382
0.3%
5491
0.1%
5551
0.1%
5591
0.1%
5601
0.1%
5612
0.3%
5641
0.1%
ValueCountFrequency (%)
8181
0.1%
8071
0.1%
7991
0.1%
7972
0.3%
7901
0.1%
7881
0.1%
7852
0.3%
7841
0.1%
7821
0.1%
7772
0.3%

repayment_history
Real number (ℝ)

Zeros 

Distinct12
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.68
Minimum0
Maximum11
Zeros63
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2026-02-20T13:33:56.466799image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4769464
Coefficient of variation (CV)0.61213844
Kurtosis-1.1631683
Mean5.68
Median Absolute Deviation (MAD)3
Skewness-0.091638119
Sum3976
Variance12.089156
MonotonicityNot monotonic
2026-02-20T13:33:56.545853image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
672
10.3%
1167
9.6%
566
9.4%
765
9.3%
1064
9.1%
063
9.0%
155
7.9%
854
7.7%
952
7.4%
349
7.0%
Other values (2)93
13.3%
ValueCountFrequency (%)
063
9.0%
155
7.9%
246
6.6%
349
7.0%
447
6.7%
566
9.4%
672
10.3%
765
9.3%
854
7.7%
952
7.4%
ValueCountFrequency (%)
1167
9.6%
1064
9.1%
952
7.4%
854
7.7%
765
9.3%
672
10.3%
566
9.4%
447
6.7%
349
7.0%
246
6.6%

transaction_count
Real number (ℝ)

Distinct182
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.98429
Minimum10
Maximum199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2026-02-20T13:33:56.646570image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile22.95
Q157
median100.5
Q3150
95-th percentile189.1
Maximum199
Range189
Interquartile range (IQR)93

Descriptive statistics

Standard deviation53.83593
Coefficient of variation (CV)0.52275869
Kurtosis-1.1814277
Mean102.98429
Median Absolute Deviation (MAD)46.5
Skewness0.065344068
Sum72089
Variance2898.3073
MonotonicityNot monotonic
2026-02-20T13:33:56.749646image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2410
 
1.4%
1149
 
1.3%
1039
 
1.3%
1998
 
1.1%
238
 
1.1%
1398
 
1.1%
937
 
1.0%
327
 
1.0%
917
 
1.0%
347
 
1.0%
Other values (172)620
88.6%
ValueCountFrequency (%)
106
0.9%
111
 
0.1%
123
0.4%
132
 
0.3%
161
 
0.1%
175
0.7%
185
0.7%
197
1.0%
201
 
0.1%
212
 
0.3%
ValueCountFrequency (%)
1998
1.1%
1983
 
0.4%
1976
0.9%
1963
 
0.4%
1955
0.7%
1941
 
0.1%
1932
 
0.3%
1922
 
0.3%
1915
0.7%
1892
 
0.3%

spending_ratio
Real number (ℝ)

Distinct668
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.3125
Minimum10.41
Maximum89.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2026-02-20T13:33:56.858197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum10.41
5-th percentile14.0495
Q131.515
median52.35
Q371.7325
95-th percentile86.732
Maximum89.86
Range79.45
Interquartile range (IQR)40.2175

Descriptive statistics

Standard deviation23.370036
Coefficient of variation (CV)0.45544528
Kurtosis-1.233163
Mean51.3125
Median Absolute Deviation (MAD)20.095
Skewness-0.10474862
Sum35918.75
Variance546.15858
MonotonicityNot monotonic
2026-02-20T13:33:56.956331image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68.743
 
0.4%
83.162
 
0.3%
60.442
 
0.3%
70.292
 
0.3%
44.532
 
0.3%
80.72
 
0.3%
62.252
 
0.3%
17.522
 
0.3%
33.842
 
0.3%
86.952
 
0.3%
Other values (658)679
97.0%
ValueCountFrequency (%)
10.411
0.1%
10.431
0.1%
10.441
0.1%
10.521
0.1%
10.641
0.1%
10.721
0.1%
10.861
0.1%
10.911
0.1%
11.081
0.1%
11.11
0.1%
ValueCountFrequency (%)
89.861
0.1%
89.811
0.1%
89.71
0.1%
89.691
0.1%
89.571
0.1%
89.421
0.1%
89.411
0.1%
89.181
0.1%
88.911
0.1%
88.791
0.1%

join_date
Date

Unique 

Distinct700
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
Minimum2015-01-05 02:23:02
Maximum2023-12-28 10:57:00
Invalid dates0
Invalid dates (%)0.0%
2026-02-20T13:33:57.058312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:57.148199image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

default_flag
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0
563 
1
137 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0563
80.4%
1137
 
19.6%

Length

2026-02-20T13:33:57.398521image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-20T13:33:57.465230image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0563
80.4%
1137
 
19.6%

Most occurring characters

ValueCountFrequency (%)
0563
80.4%
1137
 
19.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0563
80.4%
1137
 
19.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0563
80.4%
1137
 
19.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0563
80.4%
1137
 
19.6%

Interactions

2026-02-20T13:33:53.447463image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:50.638943image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:51.140863image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:51.619964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:52.100999image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:52.550206image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:52.989834image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:53.513640image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:50.714273image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:51.210489image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:51.677808image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:52.161809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:52.609778image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:53.049142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:53.581279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:50.779775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:51.284306image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:51.744351image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:52.224881image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:52.671760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:53.117225image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:53.789016image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:50.855284image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:51.354749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:51.813309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:52.288259image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:52.736629image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:53.182039image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:53.854362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:50.914390image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:51.419111image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:51.884211image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:52.350441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:52.798604image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:53.245419image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:53.921106image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:50.985830image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:51.481964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:51.956307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:52.410842image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:52.859338image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:53.310557image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:53.992582image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:51.062274image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:51.549102image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:52.028579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:52.480570image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:52.922970image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2026-02-20T13:33:53.376758image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2026-02-20T13:33:57.518834image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ageannual_incomecredit_scoredefault_flageducation_levelemployment_typegenderloan_amountloan_purposeregionrepayment_historyspending_ratiotransaction_count
age1.000-0.0200.0290.0640.0000.0770.052-0.0710.0640.0000.0070.0910.018
annual_income-0.0201.000-0.0480.0000.0390.0000.0000.0340.0420.044-0.003-0.0290.009
credit_score0.029-0.0481.0000.0000.0480.0470.0000.0870.0280.1010.016-0.0860.022
default_flag0.0640.0000.0001.0000.0000.0000.0000.0000.1090.0000.0820.0000.000
education_level0.0000.0390.0480.0001.0000.0000.0000.0000.0000.0000.0000.0000.000
employment_type0.0770.0000.0470.0000.0001.0000.0200.0000.0770.0000.0410.0000.059
gender0.0520.0000.0000.0000.0000.0201.0000.0290.0000.0000.0420.0000.000
loan_amount-0.0710.0340.0870.0000.0000.0000.0291.0000.0000.0000.014-0.0620.031
loan_purpose0.0640.0420.0280.1090.0000.0770.0000.0001.0000.0000.0140.0000.000
region0.0000.0440.1010.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
repayment_history0.007-0.0030.0160.0820.0000.0410.0420.0140.0140.0001.0000.001-0.013
spending_ratio0.091-0.029-0.0860.0000.0000.0000.000-0.0620.0000.0000.0011.000-0.001
transaction_count0.0180.0090.0220.0000.0000.0590.0000.0310.0000.000-0.013-0.0011.000

Missing values

2026-02-20T13:33:54.101665image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-20T13:33:54.242028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-02-20T13:33:54.396295image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

customer_idagegenderregioneducation_levelemployment_typeannual_incomeloan_amountloan_purposecredit_scorerepayment_historytransaction_countspending_ratiojoin_datedefault_flag
0CUST100059.0OtherNorthGraduateSalaried778076.63308847.63Home603.0017955.692023-06-27 07:33:000
1CUST100149.0FemaleEastGraduateSalaried715041.00367030.88Home672.0104810.722017-06-24 15:17:321
2CUST100235.0FemaleWestPost-GraduateSalaried700133.14248617.62Other656.0114830.622022-11-05 08:27:530
3CUST100363.0FemaleNorthSecondaryNaN609954.74325569.57OtherNaN54542.552016-01-07 10:41:090
4CUST100428.0FemaleNorthGraduateUnemployed601412.63155590.12Business671.056146.812019-03-13 07:12:071
5CUST100541.0MaleWestGraduateSalaried467935.77269008.31Education705.024033.812020-03-18 16:40:031
6CUST100659.0MaleWestGraduateSalaried739765.68391532.43Home680.0114212.642015-07-28 23:58:160
7CUST100739.0MaleSouthGraduateSalaried684194.59323545.94Other695.0315057.212016-10-30 20:31:390
8CUST100843.0FemaleEastGraduateSelf-Employed698403.77212020.31Home771.01115551.932016-10-08 17:47:391
9CUST100931.0FemaleWestPost-GraduateSalaried494793.03333632.46Other663.0117832.642020-01-05 02:53:340
customer_idagegenderregioneducation_levelemployment_typeannual_incomeloan_amountloan_purposecredit_scorerepayment_historytransaction_countspending_ratiojoin_datedefault_flag
690CUST169061.0MaleEastGraduateSelf-Employed226051.66206694.78Other656.027761.842018-12-13 17:52:401
691CUST169155.0FemaleEastGraduateSelf-Employed522295.84387410.64Other639.0915137.102020-11-03 21:10:510
692CUST169221.0MaleWestPost-GraduateSalaried638084.78374531.04Education646.0012647.602020-08-10 21:50:160
693CUST169341.0FemaleEastGraduateSalaried689843.74460465.72Other612.01111578.632016-05-15 05:19:370
694CUST169426.0MaleWestGraduateSelf-Employed498038.66120045.37Car676.0310233.882022-06-10 03:04:280
695CUST169548.0MaleEastGraduateSalaried606888.20-47421.58Business596.097483.902023-09-22 21:06:530
696CUST169637.0OtherSouthGraduateSelf-Employed102334.53428702.13Business690.0117728.382016-07-17 03:17:520
697CUST1697NaNMaleNorthPrimaryUnemployed468350.32175770.56BusinessNaN513769.962023-11-09 18:15:090
698CUST169851.0FemaleSouthSecondarySalaried690701.74-2773.18Business687.0107260.442023-02-17 02:30:070
699CUST169925.0MaleNorthSecondarySalaried403541.19321759.56Education654.01112269.972019-11-08 23:54:551